The Generation Challenge Programme Platform: Semantic Standards
and Workbench for Crop Science
Bruskiewich, R., Senger, M., Davenport, G., Ruiz, M., Rouard, M., Hazekamp,
T., ... & van Hintum, T. (2008). The generation challenge programme platform:
semantic standards and workbench for crop science. International Journal of Plant
Genomics, 2008, 369601. doi:10.1155/2008/369601
10.1155/2008/369601
Hindawi Publishing Corporation
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Hindawi Publishing Corporation
International Journal of Plant Genomics
Volume 2008, Article ID 369601, 6 pages
doi:10.1155/2008/369601
Research Article
The Generation Challenge Programme Platform:
Semantic Standards and Workbench for Crop Science
Richard Bruskiewich,1 Martin Senger,1 Guy Davenport,2 Manuel Ruiz,3 Mathieu Rouard,4
Tom Hazekamp,4 Masaru Takeya,5 Koji Doi,5 Kouji Satoh,5 Marcos Costa,6 Reinhard Simon,7
Jayashree Balaji,8 Akinnola Akintunde,9 Ramil Mauleon,1 Samart Wanchana,1,10 Trushar Shah,2
Mylah Anacleto,1 Arllet Portugal,1 Victor Jun Ulat,1 Supat Thongjuea,10 Kyle Braak,2 Sebastian Ritter,2
Alexis Dereeper,3 Milko Skofic,4 Edwin Rojas,7 Natalia Martins,6 Georgios Pappas,6 Ryan Alamban,1
Roque Almodiel,1 Lord Hendrix Barboza,1 Jeffrey Detras,1 Kevin Manansala,1
Michael Jonathan Mendoza,1 Jeffrey Morales,1 Barry Peralta,1 Rowena Valerio,1 Yi Zhang,1
Sergio Gregorio,1,11 Joseph Hermocilla,1,11 Michael Echavez,1,12 Jan Michael Yap,1,12 Andrew Farmer,13
Gary Schiltz,13 Jennifer Lee,14 Terry Casstevens,15 Pankaj Jaiswal,15 Ayton Meintjes,16 Mark Wilkinson,17
Benjamin Good,18,19 James Wagner,18,19 Jane Morris,16 David Marshall,14 Anthony Collins,7
Shoshi Kikuchi,5 Thomas Metz,1 Graham McLaren,1 and Theo van Hintum20
1 Crop
Research Informatics Laboratory, International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila, Philippines
Research Informatics Laboratory, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641,
06600 Mexico, DF, Mexico
3 Centre International de Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, 34398 Montpellier,
Cedex 5, France
4 Bioversity International, Via dei Tre Denari 472/a, 00057 Maccarese (Fiumicino), Rome, Italy
5 National Institute for Agrobiological Sciences (NIAS), Kannondai 2-1-2, Tsukuba, Ibaraki 305-8602, Japan
6 Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA), Parque Estação Biologia Final W5 Norte, 70770-900 Brasilia, DF, Brazil
7 Centro Internacional de la Papa (CIP), Avenida La Molina 1895, La Molina, Apartado Postal 1558, Lima 12, Peru
8 International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Andhra Pradesh 502324, India
9 International Center for Agricultural Research in the Dry Areas, P.O. Box 5466, Aleppo, Syria
10 National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Phahonyothin Road, Klong 1,
Klong Luang, Pathumthani 12120, Thailand
11 Institute of Computer Science, College of Arts and Sciences, University of the Philippines, Los Baños, Laguna 4031, Philippines
12 Department of Computer Science, University of the Philippines, Room 215, Melchor Hall, Diliman, Quezon City 1101, Philippines
13 National Center for Genome Resources, 2935 Rodeo Park Drive East, Santa Fe, NM 87505, USA
14 Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK
15 Department of Plant Breeding, Cornell University, Ithaca, NY 14853, USA
16 African Centre for Gene Technologies, P.O. Box 75011, Lynnwood Ridge 0040, South Africa
17 Department of Medical Genetics, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada V6T 1Z3
18 School of Computing Science, Simon Fraser Universtiy, 8888 University Drive, Burnaby, BC, Canada V5A 1S6
19 Bioinformatics Graduate Program, Genome Sciences Centre, BC Cancer Agency, 100-570 West 7th Avenue, Vancouver,
BC, Canada V5Z 4S6
20 Centre for Genetic Resources, The Netherlands (CGN), P.O. Box 16, 6700 AA Wageningen, The Netherlands
2 Crop
Correspondence should be addressed to Richard Bruskiewich, r.bruskiewich@cgiar.org
Received 22 September 2007; Accepted 14 December 2007
Recommended by Chunguang Du
The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the
application of comparative biology and genetic resources characterization to plant breeding. A key consortium research activity
is the development of a GCP crop bioinformatics platform to support GCP research. This platform includes the following: (i)
shared, public platform-independent domain models, ontology, and data formats to enable interoperability of data and analysis
flows within the platform; (ii) web service and registry technologies to identify, share, and integrate information across diverse,
globally dispersed data sources, as well as to access high-performance computational (HPC) facilities for computationally intensive,
2
International Journal of Plant Genomics
high-throughput analyses of project data; (iii) platform-specific middleware reference implementations of the domain model
integrating a suite of public (largely open-access/-source) databases and software tools into a workbench to facilitate biodiversity
analysis, comparative analysis of crop genomic data, and plant breeding decision making.
Copyright © 2008 Richard Bruskiewich et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
1.
INTRODUCTION
The fast-moving fields of comparative genomics, molecular
breeding, and bioinformatics have the potential to bring new
knowledge to bear on problems encountered by resourcepoor farmers. These problems include abiotic stresses (such
as drought and soil salinity) and biotic stresses (such as
plant diseases and pests). The Generation Challenge Programme (GCP; http://www.generationcp.org/) aims to exploit advances in molecular biology to harness the rich
global heritage of plant genetic resources and contribute
to a new generation of stress-tolerant varieties that meet
the needs of these farmers and the consumers of their
crops. The GCP brings together three sets of partners:
member agricultural research institutes of the Consultative Group on International Agricultural Research (CGIAR;
http://www.cgiar.org/), advanced research institutes in developed countries, and national agricultural research and extension systems in developing countries, to undertake a longterm program of globally integrated scientific research, capacity building, and delivery of products for the above goal.
Central to GCP activities is the development of an integrated platform of molecular biology and bioinformatics
tools to be applied to the research objectives of the GCP.
The resulting platform is also intended to be a “global public
good” to be made freely available to all crop researchers and
breeders around the world, thus enabling agricultural scientists, particularly in developing countries, to more readily apply information about elite genetic stocks, genomic knowledge, and new breeding technologies that are becoming available to their local breeding programmes.
The goal of this GCP crop informatics platform is to
provide solutions for priority end-user needs for biodiversity analysis, comparative analysis of crop genomic data, and
plant breeding decision making. Development of the platform is driven by the following observations:
(i) GCP partners (and the international crop research
community in general) are globally distributed, each
research team having relatively large datasets to share
and integrated datasets that reside in diverse online,
but locally curated databases;
(ii) GCP research covers a diversity of crop species;
(iii) GCP research spans a wide range of scientific data
types, including germplasm, genomic, phenotypic, as
well as crop physiological and geographic information,
this constellation of data types is evolving with time as
new experimental technologies are created;
(iv) GCP scientists (and crop scientists in general) need to
apply a wide range of analytical tools already used by
their research communities; they also need new tools
to meet new or evolving needs; integration of such
tools to interoperate with one another is a nontrivial
task.
A GCP crop information platform is being developed to
better meet these challenges by managing genetic resources,
genomics, and crop information using the following components:
(i) shared public platform-independent set of scientific
domain models, ontology, and data templates to crosslink all data types and analysis processes within the
platform;
(ii) GCP domain model-constrained web service and registry technologies to identify, share, and manage the
analysis of information, as well as to integrate it across
a network of diverse globally dispersed data sources
connected to the Internet;
(iii) reference implementations of platform-specific middleware using the GCP domain model;
(iv) a suite of open-source software tools (adopted or
newly developed) integrated into a workbench and
accessing web-connected data sources. Included in
this suite is software to provide enhanced access to
high-performance computational (HPC) grid facilities enabling computationally intensive and/or highthroughput analyses of project data.
This paper will survey progress on some of the central
components of the platform, with a special emphasis on the
domain model, a reference Java middleware implementation,
and Internet protocol aspects of the project.
2.
2.1.
MATERIALS AND METHODS
GCP domain model
To cope with the scope, diversity, and dispersion of crop information, GCP researchers formulated a vision to specify
a consensus blueprint of a scientific domain model and associated ontology. The resulting models and ontology allow
a “model-driven architecture” for the development of GCP
software and network protocols [1].
The domain model is documented in Unified Modeling
Language (UML). Computable versions of the UML model
are archived in the DemeterUML folder of the “Pantheon”
project in CropForge (http://cropforge.org/) software project
repository. The UML diagrams themselves are indexed and
published with supporting narratives on a project website (http://pantheon.generationcp.org/demeter). The bulk
Richard Bruskiewich et al.
of the models are specified with the UML <<interface>>
stereotype.
At the heart of the domain model are generic core model
interfaces from which other specific scientific model interfaces are derived. This core model starts with the concept of simple identification of data objects in the system
(using the SimpleIdentifier interface), which is extended by
several more specific interfaces. The core includes a general concept of Entity, which serves as the superclass for
most other interfaces describing major scientific concepts
or data types in the system. The Entity interface documents
generic metadata about objects in the system, including specific annotation of object characteristics using a rich Feature model. Other packages in the core models provide utility models for ontology, publication, and experimental study
management.
Additional scientific models are derived as extensions
of the core models. For example, the base interface classes
of most specific major concepts or experimental objects
in the scientific domain of discourse of the GCP, such as
Germplasm, Map, or GeneProduct, directly extend the Entity model, adding subdomain-specific attributes as required.
More lightweight concepts in the system extend simpler interfaces such as Feature.
For the elaboration of specific components of the core, as
well as scientific domain models, the project generally adapts
extant public domain models. For example, the Germplasm
and Study subdomain models are derived from the data
models of the open-source International Crop Information
System (ICIS, http://www.icis.cgiar.org/; [2–4]). Aspects of
the genotype (and associated genetic map and genomic sequence) models are influenced by public initiatives such as
the Chado relational database schemata of the Generic Model
Organism Database (GMOD) project [5]. The productionrelease GCP domain model is being validated based on feedback from project scientists and developers, who are striving
to validate the model by practical application in data management and platform implementation.
A significant feature of the domain model is the reliance on extensible controlled vocabulary and ontology
(CVO) to define the full semantics of specialized types, feature attributes, and annotation values of instances of the
model classes. Where possible, the GCP is simply adopting existing CVO standards, such as from the gene ontology [6], plant ontology [7], and Microarray Gene Expression Data Society (MGED) ontology [8] consortia. Where
no appropriate ontology has yet been formalized, new dictionaries of terms are being compiled in collaboration with
GCP scientists. CVO dictionaries selected for the platform
are being catalogued in a dedicated online database (at
http://pantheon.generationcp.org/) with web browser and
web service access. Each selected dictionary is assigned a GCP
ontology index number to facilitate platform management
of the ontology. Where an existing public ontology already
has its own accession identifiers (e.g., GO identifiers for the
GO CVO), these identifiers are propagated into the full GCP
identifier for the corresponding CVO terms. However, newly
specified CVO lacking such a number space are assigned de
novo GCP accession identifiers.
3
2.2.
GCP platform middleware
Since a March 2006 public review of the GCP domain
model, the GCP informatics team has developed selected
technology-specific GCP implementations of the model,
primarily focusing on Java-based middleware specifying a
Model-View-Controller (MVC) architecture (see Figure 1).
Although the primary development stream of the project is
focusing on a Java language implementation, the GCP domain model is a “platform-independent model” amenable to
implementation with other computing languages and is, indeed, being used to guide some complementary work with
languages such as Perl, Javascript, and PHP. The Java-based
middleware was given the overall name “Pantheon” to account for the usage of various ancient agricultural gods
(mostly agricultural, e.g., Demeter, Ceres, Belenus, Osiris) in
the naming of the various layers and component parts of the
code base. This code base is open source and managed under
the Pantheon project in CropForge.
In addition to a Java implementation of the GCP domain
model, a Java application programming interface (API) was
specified to assist with and standardize software integration
of components within the middleware architecture. These
interfaces are collected into a core Java library called “PantheonBase” hosted as a module in the Ceres section of Pantheon (under Ceres/projects/Pantheonbase). PantheonBase
includes a simple DataSource interface for read-only query
retrieval of data from any source (local or distributed); a
DataConsumer interface to guide integration and synchronization of applications and viewers wishing to use data extracted using the middleware; and finally, a DataTransformer
interface to provide a framework for analysis and transformations (e.g., reformatting) of data. PantheonBase was deliberately designed to be essentially agnostic about the GCP
domain model per se, for maximum flexibility and possible
reuse with non-GCP-compliant data.
Additional support libraries are being provided within
Ceres to support GCP domain model-driven DataSource development. In addition to core and support libraries, the
Pantheon project provides a clearinghouse for platform and
data-type-specific components. These components include
adapters implementing the DataSource interface for specific
data sources (archived in Osiris) for various crop databases
at various GCP partner and external sites. Among others, current DataSource implementations include a wrapper
for the ICIS and for GMOD schemata (Chado, Gbrowse).
Other Pantheon components provide application support,
including a search engine, data visualization, and web service provider implementations (in Belenus). Examples of the
latter are support for NCGR ISYS [9], support for standalone applications based on Eclipse/RCP [10], and a webbased GCP domain-model-compliant web-based search engine (Koios).
2.3.
GCP network protocols
The GCP domain model is also being applied to platformspecific implementation of a GCP network based on Internet
bioinformatics data exchange protocols such as BioMOBY
4
International Journal of Plant Genomics
End user applications
Query
applications2
Web service
provider
MOBY,
GDPC
Pantheon
domain model + API
Middleware
Data sources
Data
consumers3
Internet
Views
Data
entry
tools1
Custom
formatted
data
files
Web service
data source
Local
database
SoapLab
1. Data template loading tool
2. Standalone/web query interfaces (e.g. genomedium, Koios)
3. Specialised data viewers
MOBY, GDPC,
Biocase/TAPIR
High
performance
computer
Figure 1
[11], SoapLab [12], SSWAP [13], and Tapir [14]. In this paper, in the interest of brevity, we will discuss only BioMOBY,
arepresentative protocol being used in the GCP network.
For BioMOBY, data types were designed using GCP domain model semantics. Although generally faithful in translating the semantics of the Demeter UML specification of
the domain model (i.e., the SimpleIdentifier interface is represented as a GCP SimpleIdentifier data type), the GCP
BioMOBY data types simplify the data representation as a
concession to BioMOBY design constraints and to web service performance.
One key example of this is the extensive substitution of
GCP SimpleIdentifier objects, instead of fully detailed data
objects, at the end of model-to-model association edges
found in the Demeter model. The rationale for this is the expectation that, in most cases, web services can apply a concept of “lazy loading” of data-type components, in which
one identifies what objects might be embedded in a parent object, but does not necessarily retrieve their details until the user needs them (as a separate web service accepting
a GCP SimpleIdentifier of the object but returning the fully
populated complex object of the specified type).
UML diagrams with supporting explanatory narration
for these GCP-specific BioMOBY data types are published on the Pantheon website (http://pantheon.generationcp.org/moby), which is complemented by a website documenting GCP BioMOBY implementation details (http://
moby.generationcp.org/). Supporting the BioMOBY protocol in Pantheon are a series of Pantheon modules for interconversion between GCP MOBY data types and Demetercompliant Java objects, for web service provider implementation, and for a MOBY client DataSource adapter to communicate with GCP-compliant web service providers.
Using GCP model-constrained BioMOBY data types (all
prefixed with “GCP ” in their name in the MOBY central registry), various GCP teams are deploying GCP-compliant web
services from a common proposed list of documented web
service use cases. Concurrently, the MOBY client DataSource
adapter is being elaborated to communicate with these web
services and import remote data into local “workbench” instances of the GCP platform.
2.4.
Additional tools integrated into the GCP platform
The GCP domain model and associated platform middleware is not an end in itself. Rather, the goal of these informatics products is to serve as a semantically and operationally rich scaffold for the integration of both local and
remote (Internet-connected) bioinformatics data resources
and analysis tools.
In addition to data sources and tools already mentioned
above, additional open-source third-party analysis tools already coded using Java, but agnostic concerning the GCP
framework are being connected to the platform through targeted software engineering. To this end, GCP developers are
connecting several public open-source applications by writing suitable DataSource adapters, DataConsumer, or DataTransformer integration code. These include Java software
hosted by GMOD such as the Apollo genome browser [15],
tools forming part of the Genomic Diversity and Phenotype
Connection (GDPC) protocol such as Tassel [16], and tools
such as TIGR Multiple Experiment Viewer [17] for microarray analysis, the Comparative Map and Trait Viewer [18]
connected to the NCGR ISYS framework [9], the Cytoscape
network visualization tool [20], and the MAXD microarray
system [19].
Richard Bruskiewich et al.
3.
RESULTS AND DISCUSSION
The GCP consortium was formally established in 2003. The
first meeting of the bioinformatics and crop informatics development team of the GCP, designated as Subprogramme
4, was hosted in Rome, in February 2004. The general
user needs and project goals were coarsely mapped out at
this meeting, with some considerable differences in opinion
voiced at how to construct the required informatics framework for the GCP. In May 2004, a smaller team of software
experts met in Mexico to discuss project management, identify key user needs and platform requirements, and make
some initial progress in the design of the system. Key decisions at this latter meeting were the adoption of the “modeldriven architecture” paradigm for system development and
to embrace web services as a key technology for global integration of systems. Numerous development meetings have
been convened annually since these initial meetings to further refine and advance the design and implementation of
the platform.
In particular, a milestone review of the GCP domain model and initial software systems using the model
was held in Pretoria, South Africa in March 2006. Since
that time, a number of early release versions of software systems based on GCP platform technology have become available, generally documented at http://pantheon.
generationcp.org/ and publicly downloadable from various CropForge projects.A special “communications” project
for GCP-specific projects is also available on CropForge
at the http://cropforge.org/projects/gcpcomm to further inform prospective users on the variety of such GCP software
tools now available, and provide a venue for user discussions
and feedback about the tools.
3.1. So, what can I do with the GCP platform?
The vision of the platform development team of the bioinformatics and crop informatics subprogramme of the GCP is
to establish a truly easy to use but extensible workbench providing interoperability and enhanced data access across all
GCP partner sites and, later, across the global crop research
community. As indicated above, the GCP domain model has
a scope of data type coverage that spans most of the pertinent scientific data types found in crop research from upstream laboratory experiments through germplasm manipulations, in a georeferenced characterized field setting. The
diversity of potential data sources and analysis tools is similarly large. What the platform facilitates is transparent data
flows between such data sources and tools, whether from locally administered databases or remote Internet-connection
resources.
In this light, a number of practical “use cases” may be
described in general terms, as a series of data manipulation
steps, to highlight some of the anticipated usage of the platform. As an indication of the data retrieval and analysis scope
of the GCP platform, we describe a general integrative use
case here below, in terms of a series of defined steps.
5
General GCP platform analysis use case for
crop improvement
(1) Retrieve the list of all genetic maps that include a quantitative trait locus (QTL) for a specified trait.
(2) Retrieve selected maps in the list, from a project
database or source file containing such maps.
(3) Load this into a suitable mapping tool (e.g., the comparative map and trait visualization tool, CMTV).
(4) Extract the pairs of flanking markers for the QTL.
(5) From a second (crop) database, retrieve the list of all
germplasm that have been genotyped with these flanking markers.
(6) Retrieve all the pertinent passport, genotype, and phenotype information about the germplasm in the list.
(7) In parallel to the steps (5) and (6), if available, retrieve any gene locus candidates within (genetic/physical/sequence) map intervals which are defined by
flanking markers which are molecular sequence based.
(8) Retrieve gene functional information about the gene
loci compiled in step (7).
(9) Retrieve the alleles of “interesting” genes from (8), in
the list of germplasm identified in step (5).
(10) Plot germplasm passport, genotype, and phenotype
information on geographical information maps.
(11) Retrieve information about the environmental characteristics of the geographical regions identified in step
(10).
(12) Identify germplasm, for further detailed evaluation,
which appears to be adapted to target environments,
which have promising phenotypic values identified in
step (6) and which contains target alleles of gene loci
identified in step (9).
(13) Identify genotyping (marker) systems potentially
available from step (9), for marker assisted selected
transfer of target traits from identified germplasm to
additional germplasm targets.
4.
CONCLUSIONS
The vision of the platform development team of the bioinformatics and crop informatics subprogramme of the GCP
is to establish a state-of-art but truly easy-to-use and extensible open-source workbench providing interoperability and
enhanced data access across all GCP partner sites and, by extension, the global crop research community.
Although several attempts have been made in the past to
build such globally integrative bioinformatics systems, few
have the global distribution of partners, scope of crop research, diversity of data types, and magnitude of datasets in
comparison to the GCP consortium, nor do they have the
long-term project perspective of 10 years. In addition, the
GCP platform is specifically targeted to bioinformatics for
developing world crop research, in contrast to biomedical
research, and also strives to integrate databases from many
plants and crops less well represented by well-funded model
organisms and crops.
In these respects, the GCP platform effort represents
an extremely ambitious but very useful global public good
6
resource for crop research. It is still conceded to be, in several respects, an incomplete evolving product, one with many
rough edges and incompletely met end-user needs; however,
the open-source and public nature of the project provides a
credible venue for wide participation of interested developers
and prospective end users in the future evolution and deployment of the platform.
International Journal of Plant Genomics
[13]
[14]
[15]
[16]
ACKNOWLEDGMENTS
[17]
This work is funded through the Generation Challenge
Programme (http://www.generationcp.org/), a consortium
funded by several international donors of the Consultative Group on International Agricultural Research (CGIAR;
http://www.cgiar.org/). The GCP domain model and platform development team gratefully acknowledges the technical contributions of other scientists at various GCP-funded
workshops, in particular, the following individuals: Brigitte
Courtois (CIRAD, France); Marco Bink (Wageningen University, The Netherlands); Michel Eduardo Beleza Yamagishi (EMBRAPA, Brazil); Hei Leung and Ken McNally (IRRI,
Philippines); and Marilyn Warburton (CIMMYT). Theo van
Hintum is the project leader for the GCP Subprogramme 4
on Crop Informatics. The Crop Research Informatics Laboratory is a single operational unit spanning IRRI and CIMMYT, as part of the IRRI-CIMMYT Alliance. Availability: see
http://pantheon.generationcp.org/.
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